Target application program updating method and device based on feedback information

A target application and feedback information technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of small number of training samples, poor classification effect, uneven distribution of samples, etc.
CN112748941APending Publication Date: 2021-05-04TENCENT TECH (SHENZHEN) CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TENCENT TECH (SHENZHEN) CO LTD
Publication Date
2021-05-04

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Abstract

The invention discloses a target application program updating method and device based on feedback information. The method comprises the steps that actual scene information input in a target application program and result feedback information corresponding to the actual scene information are obtained; according to the actual scene information and the result feedback information, a target neural network model in the target application program is updated, the target neural network model is a neural network model obtained by conducting model training on a pre-trained neural network model through a training sample set, the training sample set comprises at least two sets of sample scene information, the different groups of sample scene information correspond to different actual sample recognition results of the target object, the loss function used during model training corresponds to different weights in the different groups of sample scene information, and the weights corresponding to the different groups of sample scene information are in negative correlation with the number of the sample scene information in the different groups of sample scene information.
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Description

technical field

[0001] The present invention relates to the field of computers, in particular to a method and device for updating target application programs based on feedback information, a storage medium and electronic equipment. Background technique

[0002] At present, in some business scenarios with small samples, pre-training is generally required to obtain the model.

[0003] For model training, the solution is generally to deploy after offline training, that is, use the marked data to train the model, and after the official deployment goes online, the parameters of the model are fixed. However, for business scenarios with small samples, the number of training samples that can be used is small, and the distribution of samples is uneven, resulting in a poor classification effect of the model obtained in business scenarios with small samples.

[0004] In related technologies, in the small sample business scenario, due to the small number of training samples and uneven ...

Claims

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